This study aims to present a novel methodology for precisely estimating parameters of the second-order (2RC) equivalent circuit model (ECM) for lithium-ion battery (LIB) cells, emphasizing applications in electric vehicles (EVs). For power management, the accurate assessment of internal parameters and, as a result, the precise prediction of the battery’s state of charge (SOC) is a critical challenge. Lithium-ion cells are commonly modelled using an ECM. Efficient and comprehensive estimation of battery model parameters is a fundamental enabling technology for large-format lithium-ion battery packs owing to its crucial significance in battery safety and effective management. In this study, we suggested a novel hybrid approach for determining the ECM’s parameters. For improved results, we integrated the genetic algorithm (GA) and the non-linear least squares approach (LSQR). The experimental outcomes showed that the suggested hybrid algorithm outperforms the GA and LSQR approaches.